Saturday, March 15, 2008

The Case for Multiple Exits, Part2: Robustness

Today we are going to explore the robustness of the results from last week’s tests. Our goal is to determine if the relationship between each of these three exit strategies and expectancy holds up across multiple portfolios or was somehow dependant on the 300 stock portfolio we tested last week. The Case for Multiple Exits, Part1

To evaluate and compare these results, I am again suggesting the most effective way of doing this is to calculate our expectancy per dollar risk and use that as our measure. Many people gravitate towards net profit but I am going to propose why this is probably not the best way to compare results. Net profit can also be understood as Expectancy * Opportunity. If we have a system with an expectancy of $5 per trade and we get 1000 opportunities, we will have a net profit of $5000. If we have a system with an expectancy of $1 per trade that gives us 6000 opportunities, we will have a net profit of $6000. Which system is better? There isn’t really a right or wrong answer but looking only at net profit doesn’t always give us the fullest picture. If I have enough captial to make only 200 trades a year, which one is going to give me better returns?

Today’s test will be the exact same entries and exits from last week but instead of testing it on out IBD basket of stocks, we will run it on the 100 stocks that make up the Nasdaq100 and the 500 stocks that make up the S&P500. Keep in mind that the S&P portfolio has 5x the number of stocks and will more than likely present us with many more opportunities as a result. Rarely will one be capitalized to take the 1000s of trades these system tests do and that is why one should not look at net profit alone. The quantity of trades in the results give us a degree of statistical confidence in our expectancy but makes it increasingly difficult to make the number of trades required to achieve the same net profit results.

Ok, so what have we learned from all these mind-numbing charts and graphs?If we compare the results from last week with this week, we can create the following, easier-to-read-at-a-glance summary chart:

Wow, all that effort for that? What we can begin to conclude here is that the affects these exits have on expectancy is structural in nature and is probably not a random fluke curve-fit to a specific group of stocks during a specific set of conditions. The ratios between the exits and expectancy with each of the different portfolios is remarkably similar across the 900 different securities and 80,473 trades used to arrive at these results. We also might arrive at the conclusion that the basket of stocks from which we trade from has a pretty big impact also and the entry system we are using holds up pretty well with the right stops in place, particularly if we are not trying to trade a long-only system through one of the worst bear markets in rescent history.

I am not advocating one set of exits over the other. As a matter of fact, I am trying not to advocate anything on this blog. There are many, many more ways to exit trades than the three simple methods outlined here and we will look at some in the future. The last exit we have tested, the 25% Trailing Profit Stop is just one way of accomplishing the, "Let your winnders run" strategy and I am sure there are beter ways to go about it than this one. What I hope everyone can take away from this, as I certainly have, is that bad exits can turn a very good system into a pretty poor one and they matter - a lot. Design an exit strategy that works for you and what your goals and appetite for risk are, the possibilities are nearly endless.

Entry criteria was the same as the last tests, the IBD200 technical entries.

The problem I've had with random entries is that you get different results each time so the results are not reproducable. The swings are pretty wild from test to test so it difficult to draw much of a conclusion from them.